Method and device for locating a vehicle

ABSTRACT

A method and a device for locating a vehicle, able to estimate a first position of a vehicle from data that are generated by sensors of the vehicle and that are applied to a first particle filter, to store in memory, in a history, for a plurality of times, sensor data that led to the best estimations of a current position ti, to receive at the time tn a corrected GNSS position of the vehicle at a time t0, and to estimate using a second particle filter FP2 a corrected position of the vehicle at a time ti by applying the second particle filter FP2 to the received corrected GNSS position, to the data stored in memory in association with the time ti and to the dynamic characteristics of the vehicle that are associated with the time ti−1.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims priority to French Patent Application No.2000751, filed Jan. 27, 2020, the contents of such application beingincorporated by reference herein.

FIELD OF THE INVENTION

The invention belongs to the field of vehicle location, and inparticular relates to a method for correcting a current estimation ofthe position of a vehicle using an asynchronous correction datum.

BACKGROUND OF THE INVENTION

Techniques based on the study of the movements of the vehicle withrespect to its environment, for example by means of cameras, radars orlidars, are known. It is for example a question of the technique calledSLAM (Simultaneous Localization and Mapping). These techniques allow theposition of a vehicle in its environment to be determined in aparticularly precise manner. However, such systems have a tendency toaccumulate small errors that lead to a drift in the positioning system,making the positioning information unreliable over a long distance.

In order to limit the drift of such a system, it is known to usepositioning information from a GNSS (Global Navigation Satellite System)satellite. A GNSS is capable of delivering positioning information witha precision of about 10 to 15 meters in an absolute frame of reference,for example in a geodetic coordinate system such as WGS84. It will benoted that an improved precision may be obtained by using expensivesystems such as D-GPS (Differential Global Positioning System) or RTKemploying fixed stations.

It is thus possible to correct the drift of a locating system based onproprioceptive sensors of a vehicle. However, the precision of thepositioning remains limited by the lack of precision of the GNSSpositioning information.

In particular, this precision is insufficient when applications such asdriving autonomous vehicles are considered.

Thus, there is a need for a solution allowing, at low cost and at anygiven time, a precise position of a moving vehicle to be obtained.

SUMMARY OF THE INVENTION

To this end, a method is provided for locating a vehicle, comprising thefollowing steps:

-   -   For each time t_(i) of a plurality of successive times        [t₀-t_(n)]:        -   Acquiring, from at least one sensor, positioning data and            dynamic characteristics of the vehicle,        -   Estimating the position of the vehicle at the time t_(i) by            applying a first particle filter FP1 to the data acquired at            the time t_(i) and to the dynamic characteristics of the            vehicle that are associated with the time t_(i−1),        -   Storing in memory, in a history, a subset of the acquired            data in association with the time t_(i) of capture of said            data, the subset of data comprising the data that            contributed to the particles the weight of which is higher            than a threshold,    -   At the time t_(n):        -   Obtaining a corrected positioning datum corresponding to the            time T₀,        -   Estimating a corrected position of the vehicle at the time            t₀ by applying a second particle filter FP2 to the subset of            data stored in memory at the time t₀ and to the obtained            corrected positioning datum,    -   For each history datum stored in memory in the interval        [t₁-t_(n)]:        -   Estimating a corrected position of the vehicle at the time            t_(i) by applying the second particle filter FP2 to the            subset of data stored in memory at the time t_(i) and to the            dynamic characteristics of the vehicle associated with the            time t_(i−1).

It is thus proposed to estimate the position of a vehicle frompositioning data obtained from sensors of an ADAS (AdvancedDriver-Assistance System) and from a GNSS receiver and from dynamicparameters of the vehicle. Such sensors are widely available invehicles.

The position estimation is initially carried out, for a plurality ofsuccessive positions of the vehicle, by virtue of a first particlefilter.

It will be recalled here that a particle filter allows a currentposition P_(c) of a vehicle to be estimated from observations at thecurrent time (observations generated by ADAS and GNSS sensors forexample) and from a state known at the preceding time (dynamicparameters of the vehicle, for example VDY parameters, VDY standing for“Vehicle Dynamics”). The particle filter is configured to compute aplurality of particles representing hypotheses as to the positioning ofthe vehicle, and to select the hypothesis that is the most probable inlight of the state of the vehicle at the preceding time.

The method is noteworthy in that the data that served to generate themost probable hypotheses are stored in memory, in a history, inassociation with the acquisition time to which they relate.

This history is used when, at a current time T_(c), the vehicle receivescorrected GNSS positioning information relating to a position of thevehicle at a preceding time T_(p). This corrected GNSS position is moreprecise than the GNSS position obtained and used to estimate theposition of the vehicle at the preceding time T_(p): the GNSS positionof the vehicle at the time T_(p) is modified in the history, and thesuccessive positions of the vehicle between the time T_(p) and thecurrent time T_(c) are re-estimated by means of a second particle filterthat is separate from the first particle filter. In this way, thecorrected position of the vehicle at the time T_(p) is propagated to theposition of the vehicle at the current time T_(c).

Given that only the relevant data were stored in memory, in the history,the hypotheses computed by the second filter are immediately morerelevant. Furthermore, since the new estimations are based on a moreprecise initial GNSS position, the estimation of the current position isalso more precise.

This new corrected current position may then be used to predict the nextpositions of the vehicle, while waiting for a new corrected GNSSposition to become obtainable. In this way, a navigation system of avehicle continuously has a precise position available to it.

According to one particular embodiment, the step of obtaining acorrected positioning datum corresponding to the time T₀ comprises thefollowing steps:

-   -   Obtaining raw positioning data corresponding to the position of        a vehicle at the time t₀ from a satellite-positioning receiver,    -   Transmitting to a processing server positioning data captured at        the time t₀, the server being able to correct the GNSS data,    -   Receiving from the server corrected positioning data associated        with the time t₀.

Thus, the vehicle transmits “raw” GNSS data to a processing server andobtains in return a corrected GNSS position. The time taken to transferthe raw data, the time taken processing and the time taken to transferthe corrected position do not allow the datum to be used in real timebecause, at the time of reception of the corrected datum, it no longercorresponds to the current position of the vehicle. However, using thiscorrected past position, the method is able to re-estimate thesuccessive positions of the vehicle up to the current time, andtherefore to obtain an improved current position.

The use of a processing server to correct a GNSS position allowsadvantage to be taken of a computing power higher than that locatedon-board a vehicle.

According to one particular embodiment, the second particle filtercomprises a number of particles lower than the first filter.

Since only the relevant data (i.e. the data that allowed validhypotheses to be generated) was stored in memory, in the history, theamount of data to be processed by the second filter is smaller. Thus, itis possible to decrease the number of particles computed by the secondfilter so as to decrease the complexity of the computations, the amountof memory required and the power consumption, without affecting thequality of the estimations.

According to one particular embodiment, the subset of the data stored inmemory at the time t_(i) that is applied to the second particle filtercomprises data associated with particles determined by the firstparticle filter for the time t_(i+1) the weight of importance of whichis higher than a threshold.

The weights of importance are a posteriori probabilities associated withthe particles. Thus, only the (camera, radar, GNSS, etc.) signalscorresponding to particles to which high weights have been assigned arestored in the history.

Such a measure allows the amount of data input into the second particlefilter to be decreased, and thus the number of particles required forthe second filter to produce correct estimations to be decreased.

In one particular embodiment, the second filter FP2 comprises at leastone particle computed by the first filter FP1 for the time ti, theparticle being such that its weight computed by the filter FP1 is higherthan a threshold.

In this way, the second particle filter does not need to recompute allthe particles: the particles computed and validated by the first filter,i.e. for example the particles computed for a given time the weight ofwhich is higher than a threshold, are “imported” into the second filterduring the computation of the particles for the same given time.Furthermore, the second filter FP2 requires fewer particles to run for alonger time (from the time to which the received corrected datumcorresponds to the current time) because aberrant particles are filteredby the first filter FP1.

Such a measure allows the complexity of the processing operations to befurther decreased by reusing best estimations of the first filter ashypotheses in the estimations of the second filter.

According to one particular embodiment, the method furthermore comprisesa step of transmitting, to a server, a correction datum representativeof a discrepancy between the positioning of the vehicle estimated by asensor at a time ti and the position estimated by the second filter FP2for the time ti.

Thus, for example, a vehicle may transmit the discrepancy observedbetween the position delivered by a GNSS receiver (or an ADAS camera, aradar, etc.) and the current position estimated by the second particlefilter to a server. The server may thus establish a map of thecorrections and transmit this map to other vehicles driving in aneighboring space-time. Thus, a vehicle may obtain, from the server, adatum allowing it to correct a position delivered by its GNSS receiverfor example.

According to another aspect, the invention relates to a device forlocating a vehicle comprising a plurality of sensors, a memory and aprocessor, the processor being configured by instructions stored in thememory to implement the following steps:

-   -   For each time t_(i) of a plurality of successive times        [t₀-t_(n)]:        -   Acquiring, from at least one sensor, positioning data and            dynamic characteristics of the vehicle,        -   Estimating the position of the vehicle at the time t_(i) by            applying a first particle filter FP1 to the data acquired at            the time t_(i) and to the dynamic characteristics of the            vehicle that are associated with the time t_(i−1),        -   Storing in memory, in a history, a subset of the acquired            data in association with the time t_(i) of capture of said            data, the subset of data comprising the data that            contributed to the particles the weight of which is higher            than a threshold,    -   At the time t_(n):        -   Obtaining a corrected positioning datum corresponding to the            time T₀,        -   Estimating a corrected position of the vehicle at the time            t₀ by applying a second particle filter FP2 to the subset of            data stored in memory at the time t₀ and to the obtained            corrected positioning datum,    -   For each history datum stored in memory in the interval        [t₁-t_(n)]:        -   Estimating a corrected position of the vehicle at the time            t_(i) by applying the second particle filter FP2 to the            subset of data stored in memory at the time t_(i) and to the            dynamic characteristics of the vehicle associated with the            time t_(i−1).

According to yet another aspect, the invention relates to a vehiclecomprising a locating device such as described above.

Lastly, the invention relates to a processor-readable data medium onwhich is stored a computer program comprising instructions for executingthe steps of a locating method such as described above.

The data medium may be a non-transient data medium such as a hard disk,a flash memory or an optical disk for example.

The data medium may be any entity or device capable of storinginstructions. For example, the medium may comprise a storing means, suchas a ROM, RAM, PROM, EPROM, a CD ROM or even a magnetic storing means, ahard disk for example.

Furthermore, the data medium may be a transmissible medium such as anelectrical or optical signal, which may be transmitted via an electricalor optical cable, via radio or via other means.

Alternatively, the data medium may be an integrated circuit into whichthe program is incorporated, the circuit being able to execute or to beused in the execution of the method in question.

The various aforementioned embodiments or implementation features may beadded independently or in combination with one another, to the steps ofthe locating method.

The devices, vehicles and data media have at least advantages analogousto those conferred by the method to which they relate.

BRIEF DESCRIPTION OF THE DRAWINGS

Other features, details and advantages of the invention will becomeapparent on reading the following detailed description and on analyzingthe appended drawings, in which:

FIG. 1 shows an environment able to implement the locating methodaccording to one particular embodiment,

FIG. 2 illustrates positions of a vehicle estimated by a first particlefilter,

FIG. 3 is a flowchart illustrating the main steps of a locating methodaccording to one particular embodiment of the invention,

FIG. 4 shows corrected positions of a vehicle estimated by a secondparticle filter, and

FIG. 5 shows the architecture of a locating device able to implement thelocating method according to one particular embodiment.

DETAILED DESCRIPTION OF ONE EMBODIMENT

FIG. 1 shows a vehicle 100 driving on a road network 101. The vehicle isequipped with an ADAS comprising, for example, a radar 102. Of course,the ADAS may comprise other sensors, such as one or more cameras,lidars, etc. The ADAS is configured to analyze the signals acquired bythe sensors and to determine a trajectory of the vehicle with respect toan initial position. Thus, the radar 102 of the vehicle 100 allows arelative positioning of the vehicle with respect to its environment tobe determined.

The vehicle 100 furthermore comprises a GNSS receiver that is able toreceive raw signals from satellites 103 and to determine, from ananalysis of these signals, an absolute position of the vehicle. Forexample, the GNSS receiver allows the vehicle to determine its latitudeand its longitude in a geodetic frame of reference with a margin oferror of about ten meters.

The vehicle 100 also comprises communication means, for example a 3G, 4Gor 5G cellular network interface 104, allowing it to access acommunication network 105 via a cellular access network 106. Inparticular, these communication means allow the vehicle to exchangemessages with a processing server 107 of the network 105.

The vehicle 100 lastly comprises a memory and a computer, for example anECU (Electronic Control Unit), able to obtain operating parameters ofthe vehicle via a communication bus, for example a CAN (Controller AreaNetwork) bus, to which are connected various sensors allowing the stateof the vehicle, such as for example the angular velocity of each wheel,the orientation of the front wheels, or even lateral and longitudinalaccelerations, to be determined.

The computer of the vehicle 100 is configured, by computer-programinstructions stored in the memory, to estimate, at regular intervals,for example every 60 milliseconds, a position of the vehicle.

The position of the vehicle is estimated by means of a first particlefilter FP1, to which the location data obtained by the GNSS receiver,and the data obtained via the sensors of the vehicle (ADAS sensors, CANsignals), are delivered as input.

It will be recalled that, given observations, a particle filter seeks torepresent a distribution of probabilities conditional on a hidden state.Such a filter employs a system of particles that are characterized bypositions and weights. Thus, to estimate the position of the vehicle ata time T, a particle filter computes particles representing hypothesesas to the position of the vehicle. These hypotheses are generated fromthe state of the vehicle at the time T−1.

Thus, a particle corresponds to one possible change in the position ofthe vehicle at a time T given its state at a preceding time T−1. Whenobservations are available for the time T, for example observationsobtained from sensors of the ADAS of the vehicle or a GNSS location, thecompatibility of each particle with this observation is evaluated byvirtue of a likelihood function: each particle is then assigned a weightproportional to the computed value. The particles are then eliminated ormultiplied depending on their weight, in a second iteration.

For a particular sensor, the likelihood function is for example aGaussian distribution applied to the errors, i.e. the discrepancy,between data acquired by the sensor at a time T and synthesized dataestimated for a time T from a state at the time T−1.

FIG. 2 shows the state of the vehicle 100 at times T−2, T−1 and T. Atthe time T−1, the particle filter FP1 computes a plurality of particles,1000 particles for example, from the state 200 of the vehicle at thetime T−2. The state of the vehicle 100 at a given time is described by astate vector Ve for example containing one or more elements among thelast estimated position of the vehicle, its orientation, the angularvelocity of each wheel, the orientation of the front wheels, or evenlateral and longitudinal accelerations.

For each of the computed particles, the particle filter FP1 computes aweight. The weight of a particle at the time T−1 corresponds to theprobability that the vehicle is located in the corresponding positiongiven its state at the preceding time T−2. Thus, on the basis of thedata captured by the ADAS and/or GNSS sensors of the vehicle, weightsare associated with each of the particles.

In the example of FIG. 2 , considering the particle 202, a very lowweight is attributed to the particle 202 for example because it does notcorrespond to the GNSS and/or ADAS observations. In contrast, a highweight is computed for the particle 201 because its position agrees withthe GNSS and ADAS observations. The particle filter thus estimates thatthe most probable location of the vehicle is represented by the particle201. The particle 202 for its part is eliminated. The particle 201, withwhich is associated the state of the vehicle at the time T−1 (which isobtained via the CAN sensors), will serve as basis for the estimation ofthe position of the vehicle at the time T using a similar method.

When a particle is eliminated, like the particle 202, a new particle iscreated during the estimation of the position at the following time.When a particle is validated at a time T−1, like the particle 203, theparticle filter applies thereto a transformation to determine itsposition 204 at the time T. The applied transformation is determinedfrom the state vector Ve of the vehicle at the time T−1.

The estimation of the current position may also comprise one or moreadditional iterations allowing the estimation to be refined. To do this,in a second iteration, the particles the weight of which is lowest areeliminated and a new set of particles is computed about the particle theweight of which is highest. New weights are then computed for theparticles of this new set, the particle of highest weight being selectedas estimation of the position of the vehicle.

The locating method will now be described with reference to FIG. 3 .

In a first step 300, an initial position of the vehicle in a geodeticframe of reference is determined from a location datum obtained, forexample, via a GNSS receiver of the vehicle. This initial positionallows a first particle filter FP1 to be initialized by defining itsposition at a time T−2.

In the initializing step, the vehicle furthermore obtains operatingparameters of the vehicle at the time T−2 from sensors connected to aCAN bus. These operating parameters allow a state vector Ve1 of thevehicle, which for example comprises the orientation and the velocity ofthe drive wheels at the time T−2, to be established.

In step 301, the vehicle obtains, at a time T−1, positioning data fromvarious sensors. In particular, the vehicle obtains a new GNSS positionfrom a suitable receiver, and positioning data relating to theenvironment from proprioceptive sensors, such as sensors of an ADAS. Thevehicle furthermore establishes a new state vector Ve2, which forexample comprises the orientation and the velocity of the drive wheelsat the time T−1.

In a step 302, a position of the vehicle at the time T−1 is estimated bythe first particle filter FP1. To do this, a set of particles arecomputed by the particle filter FP1 for the time T−1. These particlesare computed from the state vector Ve1 defining the state of the vehicleat the time T−2. These particles represent various hypotheses as to theposition of the vehicle given the state vector Ve1.

A weight is then attributed to each of the particles depending on theirlikelihood with regard to the GNSS and ADAS positioning data obtained instep 301.

At the end of step 302, the estimation of the position of the vehiclecorresponds to the position of the particle of highest weight.

In a step 303, the vehicle stores in memory, in a history, the datagenerated by sensors that contributed to the computation of theparticles the weight of which is higher than a threshold.

To do this, a discrepancy, or an error, between the synthesized data ofthe particle, i.e. the characteristics of the particle at the time Tthat were estimated from a state of the vehicle at the time T−1, and thedata acquired at the time T by each sensor, is computed. The lower theerror, the better the contribution of the corresponding sensor to theevaluation of the weight.

The error is for example a metric error between the position of anobject, which position is computed from data generated by sensors (forexample the position of a panel estimated from an image captured by acamera at a time T) and a simulation of the object in the synthesizeddata (for example the position of the same panel estimated in step T−1)assuming a new state, for example a new position, of the vehicle at thetime T.

The weight of a particle is determined from the error computed for eachsensor. The weight thus allows the relevant states of the vehicle to beselected. The error for its part allows the contribution of the sensorsto the evaluation of a weight to be defined.

Thus, when the error computed at a time T for a sensor and a particle islow, the data of the sensor are stored in memory in association with atimestamp corresponding to the time of capture. For example, if theanalysis of an image generated by an ADAS camera at the time T−1indicates that the particle 202 in FIG. 2 is likely, but this particlehas been assigned a low weight because it is too different from the GNSSobservation, the image of the ADAS camera is not stored in memorybecause it has manifestly resulted in a positioning error (for examplebecause the camera is occulted at this time). In contrast, if forexample the analysis of a signal generated for example by a radar of thevehicle has allowed the particle 201 the weight of which is highest tobe estimated, the radar signal is stored in memory in association with atimestamp corresponding to the time of capture. In this way, only thedata relevant to the position of the vehicle are stored in memory in thehistory.

The history may be stored in memory in a moving time window, for examplein a window of 5 seconds. This duration may be adjusted depending onvarious constraints such as the time required to obtain a corrected GNSSposition, a network latency, the performance of the estimations carriedout by the first filter FP1, and the memory available to store thehistory. Thus, when new data are stored in memory, the oldest data aredeleted from the history.

Steps 301, 302 and 303 may be repeated for a plurality of successivetimes in order to estimate various successive positions of the vehicle.

In a step 304, the vehicle obtains, at a time T, a corrected GNSSlocation corresponding to the location of the vehicle at a precedingtime, for example at the time T−1. The corrected location datum is forexample received in a message comprising at least the latitude and thelongitude of the vehicle at the time T−1, and a timestamp datumindicating the time of capture of the location datum. Thus, the vehiclehas available to it a new GNSS datum regarding the time T−1, this newdatum being more precise than the datum obtained via the GNSS receiverof the vehicle at the time T−1.

The vehicle takes into account the corrected GNSS position relating tothe time T−1 received at the time T to estimate its current position atthe time T more precisely.

To do this, the vehicle implements a second particle filter FP2. In oneparticular embodiment, the filter FP2 comprises a number of particleslower than the number of particles of the first filter FP1. For example,the second filter comprises 10 times fewer particles than the firstfilter FP1, i.e. 100 particles.

In a step 305, the vehicle uses the timestamp datum associated with thecorrected positioning datum to search, in the history, for signals thatwere stored in memory at the time at which the vehicle occupied thecorrected position, i.e. in this example at the time T−1. It will berecalled that only the signals associated with validated particles, i.e.particles to which a weight higher than a threshold has been assigned,have been stored in memory in the history. Thus, the amount of signalsobtained from the history for the time T−1 is lower than the amount ofdata captured in step 301 for the time T−1. When a GNSS positioningdatum is present in the history for the time T−1, it is replaced by thecorrected GNSS positioning datum corresponding to this time. Thesehistory data, associated with the corrected GNSS positioning datum,allow the position of the vehicle at the time T−1 to be re-estimatedmore precisely and the second particle filter FP2 to be initialized.

To do this, the particle filter FP2 determines a set of particles thatare representative of possible positions of the vehicle at the time T−1,from the state vector Ve of the vehicle at the time T−2 stored in memoryin the history.

According to one particular embodiment, the set of particles that isdetermined by the second filter FP2 for the time T−1 comprises at leastone particle computed by the first filter FP1 for the state T−1,preferably a particle the weight of which attributed by the first filterFP1 is higher than an adaptive threshold, i.e. one that is determineddynamically. The adaptive threshold is determined so as to select, forthe second filter FP2, a subset of the particles of the filter FP1. Forexample, the threshold is determined so that 10% of the datarepresentative of the particles of the first filter FP1 are reused bythe second filter FP2. To do this, the step 303 of storing in memory, inthe history, furthermore comprises, for each position estimated by thefirst filter FP1, storing in memory at least one characteristicrepresentative of a particle the weight of which is higher than thethreshold. This particle may thus be used directly by the secondparticle filter FP2, without it being necessary to recompute it, tore-estimate the successive positions of the vehicle. Processing timesare thus optimized.

In one particular embodiment, the data transmitted from the first filterFP1 to the second filter FP2 comprise one or more of the following data:

-   at least one particle identifier, the identifier allowing a particle    to be tracked over a plurality of iterations of the first filter    FP1.-   a state vector associated with the particles the weight of which is    higher than a threshold, the threshold being determined dynamically.    An adaptive threshold makes it possible to guarantee that at least    one datum is transmitted from the first filter FP1 to the second    filter FP2. Furthermore, it is desirable to obtain a spread    distribution of the particles in the second filter FP2 in order to    limit a drift. To do this, the distribution of the weights must not    be uniform.-   at least one particle weight of the first filter FP1, allowing an    initialization of the weights of the particles of the second filter    FP2 to be initialized. In this way, it is not necessary to    initialize the second filter with arbitrarily selected default    values.-   the sensor-data history, in which sensor data relevant to the first    filter FP1 have been stored.

The vehicle then attributes weights to the particles determined for thetime T−1, on the basis of the data stored in memory, in the history, atthe time T−1, and especially on the basis of the corrected GNSS positionobtained in step 304.

FIG. 4 illustrates a corrected GNSS position 400 of the vehicle at thetime T−2 allowing an erroneous GNSS position 401 obtained via a GNSSreceiver to be corrected. It will be noted that, at the time T−1, a newset of particles have been determined (in black in the figure). This newset in particular comprises the particles 402 and 403 computed by thefirst filter FP1, and to which the filter FP1 attributed a weight higherthan a preset threshold. New particles are furthermore computed on thebasis of the state vector Ve stored in memory at the time T−2 and of thecorrected GNSS position 400.

The set of particles thus determined for the state T−1 is then weightedusing positioning data stored in memory, in the history, at the time T−1in step 303, the particles of low weight being eliminated, and theparticles of high weight being multiplied and validated in aconventional operation of the particle filter described above, in orderto obtain an estimation 404 of the position of the vehicle at the timeT−1.

From this new corrected estimation, the vehicle successively estimates,in the same way, the position of the vehicle at each intermediateposition between the corrected position and the current position forwhich data have been stored in memory, in the history, by applying thesecond particle filter FP2. By thus propagating the corrected GNSSpositioning datum, the vehicle is able to obtain an estimation 404 ofthe position of the vehicle at the current time T that is more precisethan the first estimation 405. Furthermore, the low number of signals(with respect to the number of signals captured and fed to the firstfilter) to which the second filter FP2 is applied allows the number ofparticles required to obtain a satisfactory estimation to be limited.

According to one particular embodiment, the vehicle 100 transmits rawsatellite positioning data to a correction server 107 of thecommunication network 105. To do this, the satellite-positioningreceiver of the vehicle is configured to measure a plurality ofdistances, referred to as raw satellite-positioning data, between thesatellite-positioning receiver and a plurality of satellites visible tothe satellite-positioning receiver. In one example, thesatellite-positioning receiver receives signals from a GNSSsatellite-positioning system, such as the American system GPS, theRussian system GLONASS and/or the European system GALILEO.

The raw satellite-positioning data comprise raw data that may beobtained using known methods such as phase measurement or codemeasurement, and indicators of the quality of these measurements.

The raw data are transmitted by means of a cellular communicationinterface of the vehicle, for example a 3G, 4G or 5G interface, or evena Wi-Fi® or WiMAX® interface. The vehicle transmits the raw GNSS dataregularly, for example every 5 seconds, or indeed as the need arises.The transmission interval for example depends on the infrastructure, onthe network transmission time and server processing time, or on the needto locate the vehicle. For example, the raw GNSS data are transmitted ina message in the JSON, XML or CSV format to the server 107.

On reception of the raw GNSS data, the correction server 107 performs acomputation of the position of the vehicle from the transmitted raw dataand from complementary data, for example orbital data such asephemerides data and clock correction data, and measurements relating tothe conditions of atmospheric propagation of the satellite signals, inthe ionosphere and troposphere for example.

When a corrected location corresponding to the position of the vehicle100 at a time T−2 has been determined by the correction server 107, thecorrected location is transmitted to the vehicle at a time T.

According to one particular embodiment of the invention, when thevehicle 100 obtains, in step 305, a corrected estimation of its currentposition at the time T, a value representative of a discrepancy betweenthe estimated position and the position determined by a sensor iscomputed in step 306. For example, the vehicle computes a differencebetween the corrected estimation of the position of the vehicle at thetime T and the GNSS coordinates obtained at the time T via a GNSSreceiver of the vehicle. This discrepancy value allows a correction tobe applied to the data of the GNSS receiver to obtain a reliablelocation at the current site to be determined.

The computed correction value is transmitted to a mapping server in amessage furthermore comprising the current date and the correctedestimated position of the vehicle. The server stores the correction inmemory, in a geospatial database, for example in the form of a digitalmap. This map may then be downloaded by other vehicles to correct alocation datum delivered by a sensor of the same type, in a given regionand in a given time window.

Since positioning errors are often due to environmental conditions, thecorrection value may be applied to other receivers in the samegeographical zone and in a given time window. For example, a locationerror of a GPS receiver may be due to particular ionospheric conditions.These particular conditions produce the same error in one geographiczone and at one particular date. Therefore, a correction valuedetermined by one vehicle using the method described above may be usedto correct location data of other vehicles.

FIG. 5 shows the architecture of a device able to implement the locatingmethod according to one particular embodiment of the invention.

The device 500 comprises a storage space 502, for example a memory MEM,and a processing unit 501 that is for example equipped with a processorPROC. The processing unit may be controlled by a program 503, forexample a computer program PGR, implementing the locating methodaccording to steps 300 to 305 described above.

On initialization, the instructions of the computer program 503 are forexample loaded into a RAM (Random Access Memory) before being executedby the processor of the processing unit 501. The processor of theprocessing unit 501 implements the steps of the locating methodaccording to the instructions of the computer program 503.

To do this, apart from the memory and the processor, the device 500comprises one or more sensors 505, for example a GNSS receiver, sensorsof an ADAS, such as a camera or a radar, and one or more sensors of theoperation of a vehicle into which the device is integrated, such assensors of the velocity of rotation and orientation of the wheels. Thesensors may be integrated into the device or connected thereto by acommunication bus of the vehicle, a CAN bus for example. They especiallyallow a first geographic position and a dynamic state of the vehicle ata time t_(i−1) and a representation of the environment of the vehicle ina state t_(i), to be obtained.

The device 500 comprises a first particle filter 506 (FP1), which isable to estimate the position of the vehicle at the time t_(i) from datagenerated by the sensors 505, in particular for the data acquired at thetime t_(i) and for the dynamic characteristics of the vehicle that areassociated with the time t_(i−1). The first particle filter FP1 is forexample implemented via computer-program instructions stored in thememory 502 and able to be executed by the processor 501.

The memory 502 of the device 500 is furthermore able to store a subsetof the data acquired by the sensors 505 in association with the timet_(i) of capture of said data, the subset of data comprising the datathat contributed to the particles computed by the first filter 506 (FP1)the weight of which is higher than a threshold.

The device 500 also comprises communication means 504, for example acellular network interface allowing it to connect to a cellular accessnetwork, for example a Wi-Fi®, WiMAX®, 3G, 4G or 5G access network, witha view to exchanging messages with other devices. The communicationmeans are in particular able to transmit a GNSS location datum obtainedby a GNSS receiver corresponding to the position of the vehicle at thetime t_(i) to a processing server and to receive, at a time t_(i+n), acorrection of the transmitted GNSS location datum.

The device 500 also comprises a second particle filter 507 (FP2). Thesecond particle filter is for example implemented via computer-programinstructions stored in the memory 502 of the device and able to beexecuted by the processor 501 in order to estimate a corrected positionof the vehicle at a time t₀ from the subset of data stored in the memory502 in association with the time t₀ and for the corrected positioningdatum obtained via the communication means 504. The instructions arefurthermore configured to estimate, for each history datum stored inmemory in the interval [t₁-t_(n)], a corrected position of the vehicleat a time t_(i) from the subset of data associated with the time t_(i)and from dynamic characteristics of the vehicle that are associated withthe time t_(i−1) and stored in the memory 502.

According to one particular embodiment, the device 500 is integratedinto a road vehicle, for example an automobile, a truck or even amotorcycle.

The invention claimed is:
 1. A method for locating a vehicle,comprising: For each time t_(i) of a plurality of successive times[t₀-t_(n)]: Acquiring, from at least one sensor, positioning data anddynamic characteristics of the vehicle, Estimating the position of thevehicle at a time t_(i) by applying a first particle filter FP1 to thepositioning data acquired at the time t_(i) and to the dynamiccharacteristics of the vehicle that are associated with a time t_(i−1),Based on the acquired positioning data, computing a plurality ofparticles and assigning to each particle having a weight representing aprobable position of the vehicle, Storing in memory, in a history, asubset of the acquired positioning data in association with the timet_(i) of capture of said positioning data, the subset of the acquiredpositioning data including data that contributed to particles withweight higher than a threshold, At a time t_(n): Obtaining a correctedpositioning datum corresponding to the position of the vehicle at a timet₀, the corrected positioning datum being more precise than thepositioning datum acquired from the at least one sensor of the vehicleat the time t₀, Estimating a corrected position of the vehicle at thetime t₀ by applying a second particle filter FP2 to the subset of theacquired positioning data stored in memory in association with the timet₀ and to the obtained corrected positioning datum, For each historydatum stored in memory in an interval [t₁-t_(n)]: Estimating a correctedposition of the vehicle at the time t_(i) by applying the secondparticle filter FP2 to the subset of the acquired positioning datastored in memory in association with the time t_(i) and to the dynamiccharacteristics of the vehicle associated with the time t_(i−1), andPredicting a next position of the vehicle based on the estimatedcorrected position of the vehicle.
 2. The method as claimed in claim 1,wherein the step of obtaining a corrected positioning datumcorresponding to the time t₀ comprises: Obtaining raw positioning datacorresponding to the position of a vehicle at the time t₀ from asatellite-positioning receiver, Transmitting to a processing serverpositioning data captured at the time t₀, the server being able tocorrect the GNSS data, Receiving from the server corrected positioningdata associated with the time t₀.
 3. The method as claimed in claim 1,wherein the second particle filter FP2 comprises a number of particleslower than the first particle filter FP1.
 4. The method as claimed inclaim 1, wherein the subset of the data stored in memory at the timet_(i) that is applied to the second particle filter FP2 comprises dataassociated with particles determined by the first particle filter FP1for the time t_(i) the weight of importance of which is higher than athreshold.
 5. The method as claimed in claim 1, wherein the secondfilter FP2 comprises at least one particle computed by the first filterFP1, the particle being such that its weight computed by the filter FP1is higher than a threshold.
 6. The method as claimed in claim 1, suchthat it furthermore comprises transmitting, to a server, a correctiondatum representative of a discrepancy between the positioning of thevehicle estimated by the at least one sensor at the time t_(i) and theposition estimated by the second filter FP2 for the time t_(i).
 7. Adevice for locating a vehicle comprising a plurality of sensors, amemory and a processor, the processor being configured by instructionsstored in the memory to implement the following steps: For each timet_(i) of a plurality of successive times [t₀-t_(n)]: Acquiring, from atleast one sensor from the plurality of sensors, positioning data anddynamic characteristics of the vehicle, Estimating the position of thevehicle at a time t_(i) by applying a first particle filter FP1 to thepositioning data acquired at the time t_(i) and to the dynamiccharacteristics of the vehicle that are associated with a time t_(i−1),Based on the acquired positioning data, computing a plurality ofparticles and assigning to each particle having a weight representing aprobable position of the vehicle, Storing in memory, in a history, asubset of the acquired positioning data in association with the timet_(i) of capture of said positioning data, the subset of the acquiredpositioning data including data that contributed to the particles withweight higher than a threshold, At a time t_(n): Obtaining a correctedpositioning datum corresponding to the position of the vehicle at a timet₀, the corrected positioning datum being more precise than thepositioning datum acquired from the at least one sensor of the vehicleat the time t₀, Estimating a corrected position of the vehicle at thetime t₀ by applying a second particle filter FP2 to the subset of theacquired positioning data stored in memory at the time t₀ and to theobtained corrected positioning datum, For each history datum stored inmemory in an interval [t₁-t_(n)]: Estimating a corrected position of thevehicle at the time t_(i) by applying the second particle filter FP2 tothe subset of the acquired positioning data stored in memory at the timet_(i) and to the dynamic characteristics of the vehicle associated withthe time t_(i−1), and Predicting a next position of the vehicle based onthe estimated corrected position of the vehicle.
 8. A vehicle comprisinga device for locating the vehicle, the device comprising a plurality ofsensors, a memory and a processor, the processor being configured byinstructions stored in the memory to implement the following steps: Foreach time t_(i) of a plurality of successive times [t₀-t_(n)]:Acquiring, from at least one sensor from the plurality of sensors,positioning data and dynamic characteristics of the vehicle, Estimatingthe position of the vehicle at a time t_(i) by applying a first particlefilter FP1 to the positioning data acquired at the time t_(i) and to thedynamic characteristics of the vehicle that are associated with a timet_(i−1), Based on the acquired positioning data, computing a pluralityof particles and assigning to each particle having a weight representinga probable position of the vehicle, Storing in memory, in a history, asubset of the acquired positioning data in association with the timet_(i) of capture of said positioning data, the subset of the acquiredpositioning data including data that contributed to the particles withweight higher than a threshold, At a time t_(n): Obtaining a correctedpositioning datum corresponding to the position of the vehicle at a timet₀, the corrected positioning datum being more precise than thepositioning datum acquired from the at least one sensor of the vehicleat the time t₀, Estimating a corrected position of the vehicle at thetime t₀ by applying a second particle filter FP2 to the subset of theacquired positioning data stored in memory at the time t₀ and to theobtained corrected positioning datum, For each history datum stored inmemory in an interval [t₁-t_(n)]: Estimating a corrected position of thevehicle at the time t_(i) by applying the second particle filter FP2 tothe subset of the acquired positioning data stored in memory at the timet_(i) and to the dynamic characteristics of the vehicle associated withthe time t_(i−1), and Predicting a next position of the vehicle based onthe estimated corrected position of the vehicle.